Autonomous Architecture
The autonomous architecture is new framework to design, build and understand the invisible layers. These layers are intertwined to formulate autonomous vehicles technologies at all altitudes including the autonomous actions to the software defined integrations that dictate the processing of embedded mechanics to the continuous learning capabilities.
The five layers are as follows:
1. Data Engineering
2. Algorithm Engineering
3. Machine Learning Engineering
4. Contextual Integrations
5. Autonomous Vehicles
Data Engineering
Data Engineering is the most expansive layer that embodies creation and ingestion of data while maintaining both physical and digital synchronization. The wide range and variety of data can be seen too complex to structure, capture and synthesize. However, there is an approach to simplify the framework when we assume that all data can take both digital and physical forms.
The three primary practices in data engineering discipline are as follows:
1. Data Generation: data is generated from variety of sensors, events, analysis or reports.
2. Data Twinization: each piece of data can have twin where physical data is converted to digital replica and digital data is converted to action in physical world. This process of keeping physical and digital data in sync has new term called twinization.
3. Data Harmonization: when the issues appear in twinization, search or smooth flow through data pipelines a structured approach to determine and implement solution is recommended. This new data harmonization framework has three solution categories called proactive, real time or post processing. Data harmonization practices within data engineering discipline need to be designed, built and implemented.
Algorithm Engineering
Current software algorithms within software application frameworks are characterized by associated graphical user interface on mobile, desktop, web and smart devices. The algorithm implementations are invisible with unlimited capacity however limited by user interface needed for human intervention. In autonomous architecture user interface becomes obsolete because the activates will be automatic.
The three primary practices in algorithm engineering discipline are as follows:
1. Flow Design: flow charts including decision making nodes and action blocks for physical hardware and digital software.
2. Deep Learning Models: deep learning models that can perform real time analysis, feed the future machine, trigger the issue resolution or alerts for change management.
3. Log Management: log management practice that registers the event and respective data. The logs created by algorithms as well as twinization process provide capabilities to go back to the various states for post processing analysis.
Machine Learning Engineering
Machine learning engineering is a layer at the center of the autonomous architecture. The machine learning capabilities that continuously cognize data as well as recognize algorithms components while automatically advancing both data engineering and algorithm engineering disciplines is powerful phenomenon.
Machine learning capabilities contain two dimensions:
1. Need for human intervention: Automatic or Human involvement
2. Time of implementation: Proactive, Realtime or Post processing
Machine learning capabilities rivet the data driven decisions and algorithm determined outcomes as inputs. The output of machine learning capabilities can be the input for the next machine learning capabilities.
Following three inputs forms choices are integral to machine learning capabilities discipline:
1. Physical Action: the physical world consists of senses, sensors, mechanical as well as infrastructure.
2. Written Paper: written paper can be handwritten, printed or colorful. Paper has been the most matured mechanism of communication and sharing the learnings.
3. Digital Signal: the software and internet industries are new, however the value created by digital systems and digital twins of physical systems strengthen the machine learning capabilities.
Contextual Integrations
Integrations of siloed data, multiple systems, variety of applications has been afterthought, because of departmentalization techniques deployed in organizations that need to expand at scale. Contextual integrations create an opportunity where the integrations will occur at unprecedented scale.
Following categories of contexts are fundamental in contextual integration discipline:
1. Self-sustaining Product: self-sustaining product has autonomous capabilities to perform desired functions and include all layers of autonomous architecture.
2. Evolving Ecosystem: evolving ecosystem has dynamic capabilities where various companies, products, technologies can collaborate.
3. Autonomous Lifecycle: autonomous lifecycle includes mechanism for creation, connection and cease the entities such as self-sustaining products and evolving ecosystems.
Autonomous Vehicles
Autonomous vehicles have been a dream since the brilliant minds since long times. Humans figured out that some mechanisms can operate without human interventions. Term automatic has been expanded and called autonomous in this architecture framework to incorporate the data driven decisions, algorithm determined actions and machine learning capabilities. Autonomous architecture with the contextual integrations discipline hold capacity to scale and expand at unprecedented quotient.
Following are three primary categories within autonomous vehicles discipline:
1. On Land: on land autonomous vehicles need infrastructure such as road network.
2. In Water: in water autonomous vehicles are widely used for logistics as well as long cruise stays.
3. Above Sky: above the sky provides consistent opportunities at many different altitudes around the planet earth.
Autonomous vehicles can sustain themselves with solar energy. The concept that vehicle needs a driver, captain or a pilot has become obsolete since hundreds of years. The autonomous rovers on other planetary systems have proven successful. The data received from other planets digital images as well as physical material samples is great example of the potential autonomous vehicles hold.
Autonomous architecture powered by disciplines such as data engineering, algorithm engineering and machine learning disciplines and contextual integrations is rather newer innovation.